Abstract:

This paper presents a memory-based model of direct psychophysical
scaling. The model is based on an extension of the
cognitive architecture ACT-R
and uses anchors that serve as prototypes for the stimuli
classified within each response category.
Using the ANCHOR model as a specific example, a general Bayesian
framework is introduced. It provides principled methods for making
data-based inferences about models of this kind. The internal
representations in the model are analyzed as hidden variables that
are constructed from the stimuli according to probabilistic
representation rules. In turn, the hidden representations produce
overt responses via probabilistic performance rules.
Incremental learning rules transform the model into a dynamic system.
A parameter-fitting algorithm is formulated and tested on
experimental data.